Visual search and selective attentionVisual search and selective attention Hermann J. Mu¨ller and...

Visual search and selective attention

Hermann J. Müller and Joseph Krummenacher

Ludwig-Maximilian-University Munich, Germany

Visual search is a key paradigm in attention research that has proved to be a test

bed for competing theories of selective attention. The starting point for most

current theories of visual search has been Treisman’s ‘‘feature integration theory’’ of

visual attention (e.g., Treisman & Gelade, 1980). A number of key issues that have

been raised in attempts to test this theory are still pertinent questions of research

today: (1) The role and (mode of) function of bottom-up and top-down

mechanisms in controlling or ‘‘guiding’’ visual search; (2) in particular, the role

and function of implicit and explicit memory mechanisms; (3) the implementation

of these mechanisms in the brain; and (4) the simulation of visual search processes

in computational or, respectively, neurocomputational (network) models. This

paper provides a review of the experimental work and the*often conflicting*theoretical positions on these thematic issues, and goes on to introduce a set of

papers by distinguished experts in fields designed to provide solutions to these

issues.

A key paradigm in attention research, that has proved to be a test bed for

competing theories of selective attention, is visual search. In the standard

paradigm, the observer is presented with a display that can contain a target

stimulus amongst a variable number of distractor stimuli. The total number

of stimuli is referred to as the display size. The target is either present or

absent, and the observers’ task is to make a target-present vs. target-absent

decision as rapidly and accurately as possible. (Alternatively, the search

display may be presented for a limited exposure duration, and the dependent

variable is the accuracy of target detection.) The time taken for these

decisions (the reaction time, RT) can be graphed as a function of the display

size (search RT functions). An important characteristic of such functions is

its slope, that is, the search rate, measured in terms of time per display item.

Based on the search RT functions obtained in a variety of search

experiments, a distinction has been proposed between two modes of visual

Please address all correspondence to Hermann J. Müller, Department of Psychology,

Allgemeine und Experimentelle Psychologie, Ludwig-Maximilian-University Munich,

Leopoldstrasse 13, 80802 München, Germany. E-mail: [email protected]

VISUAL COGNITION, 2006, 14 (4/5/6/7/8), 389�410

# 2006 Psychology Press Ltdhttp://www.psypress.com/viscog DOI: 10.1080/13506280500527676

search (e.g., Treisman & Gelade, 1980): Parallel and serial. If the search

function increases only little with increasing display size (search rates B 10ms/item), it is assumed that all items in the display are searched

simultaneously, that is, in ‘‘parallel’’ (‘‘efficiently’’). In contrast, if the search

functions exhibit a linear increase (search rates � 10 ms/item), it is assumedthat the individual items are searched successively, that is, the search

operates ‘‘serially’’ (‘‘inefficiently’’).

This does not explain, of course, why some searches can operate

efficiently, in parallel, while others operate inefficiently, (strictly) serially,

and why, in some tasks, the search efficiency is found to lie in between

these extremes. In order to explain this variability, a number of theories of

visual search have been proposed, which, in essence, are general theories

of selective visual attention. The starting point for most current theories

of visual search has been Anne Treisman’s ‘‘feature integration theory’’ of

visual attention (e.g., Treisman & Gelade, 1980; see below). This theory

led to a boom in studies on visual search; for example, between 1980 and

2000, the number of published studies rose by a factor of 10. A number of

key issues that have been raised in attempts to test this theory are

still pertinent questions of research today: (1) The role and (mode of)

function of bottom-up and top-down mechanisms in controlling

or ‘‘guiding’’ visual search; (2) in particular, the role and function of

implicit and explicit memory mechanisms; (3) the implementation of

these mechanisms in the brain; and (4) the simulation of visual search

processes in computational or, respectively, neurocomputational (network)

models.

The present Visual Cognition Special Issue presents a set of papers

concerned with these four issues. The papers are based on the presenta-

tions given by some 35 leading visual-search experts worldwide, from a

variety of disciplines*including experimental and neuropsychology,electro- and neurophysiology, functional imaging, and computational

modelling*at the ‘‘Visual Search and Selective Attention’’ symposiumheld at Holzhausen am Ammersee, near Munich, Germany, June 6�10,2003 (‘‘Munich Visual Search Symposium’’, for short1). The aim of this

meeting was to foster a dialogue amongst these experts, in order to

contribute to identifying theoretically important joint issues and discuss

ways of how these issues can be resolved by using convergent, integrated

methodologies.

1 Supported by the DFG (German National Research Council) and the US Office of Naval

Research.

390 MÜLLER AND KRUMMENACHER

THE SPECIAL ISSUE

This Special Issue opens with Anne Treisman’s (2006 this issue) invited

‘‘Special Lecture’’, which provides an up-to-date overview of her research,

over 25 years, and her current theoretical stance on visual search. In

particular, Treisman considers ‘‘how the deployment of attention determines

what we see’’. She assumes that attention can be focused narrowly on a

single object, spread over several objects or distributed over the scene as a

whole*with consequences for what we see. Based on an extensive review ofher ground-breaking original work and her recent work, she argues that

focused attention is used in feature binding. In contrast, distributed

attention (automatically) provides a statistical description of sets of similar

objects and gives the gist of the scene, which may be inferred from sets of

features registered in parallel.

The four subsequent sections of this Special Issue present papers that

focus on the same four themes discussed at the Munich Visual Search

Symposium (see above): I Preattentive processing and the control of visual

search; II the role of memory in the guidance of visual search; III brain

mechanisms of visual search; and IV neurocomputational modelling of

visual search. What follows is a brief introduction to these thematic issues,

along with a summary of the, often controversial, standpoints of the various

experts on these issues.

I. Preattentive processing and the control of visual search

Since the beginnings of Cognitive Psychology, theories of perception have

drawn a distinction between preattentive and attentional processes (e.g.,

Neisser, 1967). On these theories, the earliest stages of the visual system

comprise preattentive processes that are applied uniformly to all input

signals. Attentional processes, by contrast, involve more complex computa-

tions that can only be applied to a selected part of the preattentive output.

The investigation of the nature of preattentive processing aims at determin-

ing the functional role of the preattentive operations, that is: What is the

visual system able to achieve without, or prior to, the allocation of focal

attention?

Registration of basic features. Two main functions of preattentive

processes in vision have been distinguished. The first is to extract basic

attributes, or ‘‘features’’, of the input signals. Since preattentive processes

code signals across the whole visual field and provide the input information

for object recognition and other, higher cognitive processes, they are limited

to operations that can be implemented in parallel and executed rapidly.

VISUAL SEARCH AND ATTENTION 391

Experiments on visual search have revealed a set of visual features that are

registered preattentively (in parallel and rapidly), including luminance,

colour, orientation, motion direction, and velocity, as well as some simple

aspects of form (see Wolfe, 1998). These basic features generally correspondwith stimulus properties by which single cells in early visual areas can be

activated.

According to some theories (e.g., Treisman & Gelade, 1980; Wolfe, Cave,

& Franzel, 1989), the output of preattentive processing consists of a set of

spatiotopically organized feature maps that represent the location of each

basic (luminance, colour, orientation, etc.) feature within the visual field.

There is also evidence that preattentive processing can extract more complex

configurations such as three-dimensional form (Enns & Rensink, 1990) andtopological properties (Chen & Zhou, 1997). In addition, individual

preattentively registered items can be organized in groups if they share

features (Baylis & Driver, 1992; Harms & Bundesen, 1983; Kim & Cave,

1999) or form connected wholes (Egly, Driver, & Rafal, 1994; Kramer &

Watson, 1996). Based on evidence that preattentive processes can also

complete occluded contours, He and Nakayama (1992) proposed that the

output of the preattentive processes comprises not only of a set of feature

maps, but also a representation of (object) surfaces.

Guidance of attention. Besides rendering an ‘‘elementary’’ representation

of the visual field, the second main function of preattentive processes is the

guiding of focal-attentional processes to the most important or ‘‘promising’’

information within this representation. The development of models of visual

processing reveals an interesting tradeoff between these two functions: If the

output of preattentive processing is assumed to only represent basic visual

features, so that the essential operations of object recognition are left toattentional processes, focal attention must be directed rapidly to the

(potentially) most ‘‘meaningful’’ parts of the field, so that the objects

located there can be identified with minimal delay.

Preattentive processes must guarantee effective allocation of focal

attention under two very different conditions. First, they must mediate the

directing of attention to objects whose defining features are not predictable.

This data-driven or bottom-up allocation of attention is achieved by

detecting simple features (or, respectively, their locations) that differ fromthe surrounding features in a ‘‘salient’’ manner (e.g., Nothdurft, 1991). The

parallel computation of feature contrast, or salience, signals can be a very

effective means for localizing features that ought to be processed attention-

ally; however, at the same time it can delay the identification of a target

object when there is also a distractor in the field that is characterized by a

salient feature (Theeuwes, 1991, 1992). Numerous investigations had been

concerned with the question under which conditions focal attention is


‘‘attracted’’ by a salient feature (or object) and whether the mechanisms that

direct focal attention to salient features (or objects) are always and invariably

operating or whether they can be modulated by the task set (e.g., Bacon &

Egeth, 1997; Yantis, 1993).Under other conditions, the appearance of a particular object, or a

particular type of object, can be predicted. In such situations, preattentive

processes must be able in advance to set the processing (top-down) for the

corresponding object and initiate the allocation of focal attention upon its

appearance. This can be achieved by linking the allocation of attention to a

feature value defining the target object, such as blue or vertical (Folk &

Remington, 1998), or to a defining feature dimension, such as colour or

orientation (Müller, Reimann & Krummenacher, 2003). Although the top-down allocation of attention is based, as a rule, on the (conscious) intention

to search for a certain type of target, it can also be initiated by implicit

processes. If the preceding search targets exhibit a certain feature (even a

response-irrelevant feature), or are defined within a certain dimension,

attention is automatically guided more effectively to the next target if this is

also characterized by the same feature or feature dimension (Krummena-

cher, Müller, & Heller, 2001; Maljkovic & Nakayama, 1994, 2000; Müller,

Heller, & Ziegler, 1995).An important question for theories of preattentive vision concerns the

interaction between top-down controlled allocation of attention to expected

targets and bottom-up driven allocation to unexpected targets. What is

required is an appropriate balance between these to modes of guidance, in

order to guarantee that the limited processing resources at higher stages of

vision are devoted to the most informative part of the visual input. While

there is a broad consensus that preattentive processes can guide visual search

(i.e., the serial allocation of focal attention), there are a number of openquestions concerning the interaction between top-down and bottom-up

processing in the control of search, the top-down modifiability of pre-

attentive processes, the interplay of feature- and dimension-based set

(processes), etc. Further open questions concern the complexity of the

preattentively computed ‘‘features’’. All these issues are addressed by the

papers collected in the first section of this Special Issue, ‘‘Preattentive

processing and the control of visual search’’.

The first set of three papers (Folk & Remington; Theeuwes, Reimann &Mortier; Müller & Krummenacher) is concerned with the issue whether and

to what extent preattentive processing is top-down modulable.

More specifically, C. L. Folk and R. Remington (2006 this issue) ask to

which degree the preattentive detection of ‘‘singletons’’ elicits an involuntary

shift of spatial attention (i.e., ‘‘attentional capture’’) that is immune from

top-down modulation. According to their ‘‘contingent-capture’’ perspective,

preattentive processing can produce attentional capture, but such capture is


contingent on whether the eliciting stimulus carries a feature property

consistent with the current attentional set. This account has been challenged

recently by proponents of the ‘‘pure- (i.e., bottom-up driven-) capture’’

perspective, who have argued that the evidence for contingencies inattentional capture actually reflects the rapid disengagement and recovery

from capture. Folk and Remington present new experimental evidence to

counter this challenge.

One of the strongest proponents of the pure-capture view is Theeuwes.

J. Theeuwes, B. Reimann, and K. Mortier (2006 this issue) reinvestigated the

effect of top-down knowledge of the target-defining dimension on visual

search for singleton feature (‘‘pop-out’’) targets. They report that, when

the task required simple detection, advance cueing of the dimension of theupcoming singleton resulted in cueing costs and benefits; however, when the

response requirements were changed (‘‘compound’’ task, in which the target-

defining attributes are independent of those determining the response),

advance cueing failed to have a significant effect. On this basis, Theeuwes et

al. reassert their position that top-down knowledge cannot guide search for

feature singletons (which is, however, influenced by bottom-up priming

effects when the target-defining dimension is repeated across trials).

Theeuwes et al. conclude that effects often attributed to early top-downguidance may in fact represent effects that occur later, after attentional

selection, in processing.

H. J. Müller and J. Krummenacher (2006 this issue) respond to this

challenge by asking whether the locus of the ‘‘dimension-based attention’’

effects originally described by Müller and his colleagues (including their top-

down modifiability by advance cues) are preattentive or postselective in

nature. Müller and his colleagues have explained these effects in terms of a

‘‘dimension-weighting’’ account, according to which these effects arise at apreattentive, perceptual stage of saliency coding. In contrast, Cohen (e.g.,

Cohen & Magen, 1999) and Theeuwes have recently argued that these effects

are postselective, response-related in nature. In their paper, Müller and

Krummenacher critically evaluate these challenges and put forward

counterarguments, based partly on new data, in support of the view that

dimensional weighting operates at a preattentive stage of processing (without

denying the possibility of weighting processes also operating post selection).

A further set of four papers (Nothdurft; Smilek, Enns, Eastwood, &Merikle; Leber & Egeth; Fanini, Nobre, & Chelazzi) is concerned with the

influence of ‘‘attentional set’’ for the control of search behaviour.

H.-C. Nothdurft (2006 this issue) provides a closer consideration of the

role of salience for the selection of predefined targets in visual search. His

experiments show that salience can make targets ‘‘stand out’’ and thus

control the selection of items that need to be inspected when a predefined

target is to be searched for. Interestingly, salience detection and target


identification followed different time courses. Even typical ‘‘pop-out’’ targets

were located faster than identified. Based on these and other findings,

Nothdurft argues in favour of an interactive and complementary function of

salience and top-down attentional guidance in visual search (where‘‘attention settings may change salience settings’’).

While top-down controlled processes may guide selective processes

towards stimuli displaying target-defining properties, their mere involvement

may also impede search, as reported by D. Smilek, J. T. Enns, J. D.

Eastwood, and P. M. Merikle (2006 this issue). They examined whether

visual search could be made more efficient by having observers give up active

control over the guidance of attention (and instead allow the target to

passively ‘‘pop’’ into their minds) or, alternatively, by making them performa memory task concurrently with the search. Interestingly, passive instruc-

tions and a concurrent task led to more efficient performance on a hard (but

not an easy) search task. Smilek et al. reason that the improved search

efficiency results from a reduced reliance on slow executive control processes

and a greater reliance on rapid automatic processes for directing visual

attention.

The importance of executive control or (top-down) ‘‘attentional set’’ for

search performance is further illustrated by A. B. Leber and H. E. Egeth(2006 this issue). They show that, besides the instruction and the stimulus

environment, past experience (acquired over an extended period of practice)

can be a critical factor for determining the set that observers bring to bear on

performing a search task. In a training phase, observers could use one of two

possible attentional sets (but not both) to find colour-defined targets in a

rapid serial visual presentation stream of letters. In the subsequent test

phase, where either set could be used, observers persisted in using their pre-

established sets.In a related vein, A. Fanini, A. C. Nobre, and L. Chelazzi (2006 this issue)

used a negative priming paradigm to examine whether feature-based (top-

down) attentional set can lead to selective processing of the task-relevant

(e.g., colour) attribute of a single object and/or suppression of its irrelevant

features (e.g., direction of motion or orientation). The results indicate that

individual features of a single object can indeed undergo different processing

fates as a result of attention: One may be made available to response

selection stages (facilitation), while others are actively blocked (inhibition).Two further papers (Pomerantz; Cave & Batty) are concerned with visual

‘‘primitives’’ that may form the more or less complex representations on

which visual search processes actually operate*‘‘colour as a Gestalt’’ and,respectively, stimuli that evoke strong threat-related emotions.

J. R. Pomerantz (2006 this issue) argues that colour perception meets the

customary criteria for Gestalts at least as well as shape perception does, in

that colour emerges from nonadditive combination of wavelengths in the


perceptual system and results in novel, emergent features. Thus, colour

should be thought of not as a basic stimulus feature, but rather as a complex

conjunction of wavelengths that are integrated in perceptual processing. As a

Gestalt, however, colour serves as a psychological primitive and so, as with

Gestalts in form perception, may lead to ‘‘pop out’’ in visual search.

Recently, there have been claims (e.g., Fox et al., 2000; Öhman, Lundqvist

& Esteves, 2001) that social stimuli, such as those evoking strong emotions

or threat, may also be perceptual primitives that are processed preattentively

(e.g., detected more rapidly than neutral stimuli) and, thus, especially

effective at capturing attention. In their contribution, K. R. Cave and M. J.

Batty (2006 this issue) take issue with these claims. A critical evaluation of

the relevant studies leads them to argue that there is no evidence that the

threatening nature of stimuli is detected preattentively. There is evidence,

however, that observers can learn to associate particular features, combina-

tions of features, or configurations of lines with threat, and use them to

guide search to threat-related targets.

II. The role of memory in the guidance of visual search

Inhibition of return and visual marking. A set of issues closely related to

‘‘preattentive processing’’ concerns the role of memory in the guidance of

visual search, especially in hard search tasks that involve serial attentional

processing (e.g., in terms of successive eye movements to potentially

informative parts of the field). Concerning the role of memory, there are

diametrically opposed positions. There is indirect experimental evidence that

memory processes which prevent already searched parts of the field from

being reinspected, play no role in solving such search problems. In particular,

it appears that visual search can operate efficiently even when the target and

the distractors unpredictably change their positions in the search display

presented on a trial. This has given rise to the proposal that serial search

proceeds in a ‘‘memoryless’’ fashion (cf. Horowitz & Wolfe, 1998). On the

other hand, there is evidence that ‘‘inhibition of return’’ (IOR) of attention

(Posner & Cohen, 1984) is also effective in the guidance of visual search, by

inhibitorily marking already scanned locations and, thereby, conferring an

advantage to not-yet-scanned locations for the allocation of attention

(Klein, 1988; Müller & von Mühlenen, 2000; Takeda & Yagi, 2000).

Related questions concern whether and to what extent memory processes

in the guidance of search are related to mechanisms of eye movement control

and how large the capacity of these mechanisms is. For example, Gilchrist

and Harvey (2000) observed that, in a task that required search for a target

letter amongst a large number of distractor letters, refixations were rare

within the first two to three saccades following inspection of an item, but

afterwards occurred relatively frequently. This argues in favour of a short-


lived (oculomotor) memory of a low capacity for already fixated locations.

In contrast, Peterson, Kramer, Wang, Irwin, and McCarley (2001) found

that, when observers searched for a ‘‘T’’ amongst ‘‘L’’s, refixations occurred

less frequently (even after long intervals during which up to 11 distractorswere scanned) than would have been expected on the basis of a memoryless

model of visual search. This argues in favour of a longer lasting memory of

relatively large capacity.

Another, controversial form of search guidance has been proposed by

Watson and Humphreys (1997), namely, the parallel ‘‘visual marking’’ of

distractors in the search field: If, in conjunction search (e.g., for a red ‘‘X’’

amongst blue ‘‘X’’s and red ‘‘O’’s), a subset of the distractors (red ‘‘O’’s) are

presented prior to the presentation of the whole display (which includes thetarget), a search process that is normally inefficient is turned into an efficient

search. Watson and Humphreys explained this in terms of the inhibitory

marking (of the locations) of the prepresented distractors, as a result of

which search for a conjunction target amongst all distractors is reduced to

search for a simple feature target amongst the additional, later presented

distractors (search for a red ‘‘X’’ amongst blue ‘‘X’’s). However, whether

Watson and Humphreys’ findings are indeed based on the*memory-dependent*parallel suppression of distractor positions or, alternatively, theattentional prioritization of the display items that onset later (accompanied

by abrupt luminance change) (Donk & Theeuwes, 2001), is controversial.

(See also Jiang, Chun, & Marks, 2002, who argued that the findings of

Watson and Humphreys reflect a special memory for stimulus asynchronies.)

Scene-based memory. The idea, advocated by Watson and Humphreys

(1997), of an inhibitory visual marking implies a (more or less implicit)

memory of the search ‘‘scene’’. That a memory for the search scene exists isalso documented by other studies of visual search for pop-out targets

(Kumada & Humphreys, 2002; Maljkovic & Nakayama, 1996). These

studies have shown that detection of a salient target on a given trial that

appears at the same position as a target on previous trials is expedited

relative to the detection of a target at a previous nontarget (or empty)

position; in contrast, detection is delayed if a target appears at the position

of a previously salient, but to-be-ignored distractor, relative to detection of a

target at a nondistractor position. Such positive and negative effects on thedetection of a target on the current trial could be traced back across five to

eight previous trials (Maljkovic & Nakayama, 1996). The long persistence of

these effects suggests that they are based on (most likely implicit) memory

mechanisms of search guidance. That such mechanisms can also represent

the arrangement of items in complex search scenes, is suggested by Chun

and Jiang (1998). They found that the search (e.g., for an orthogonally

rotated ‘‘T’’ amongst orthogonally rotated ‘‘L’’s) on a trial was expedited if a


certain, complex arrangement of display items (targets und distractors) was

repeated, with some five repetitions of the arrangement (one repetition each

per block of 24 trials) being sufficient to generate the learning effect.

With regard to scene-based memory, another controversial issue is: Howmuch content-based information is retained from the (oculomotor) scanning

of a natural scene in an enduring (implicit or explicit) representation? One

position states that visual (object) representations disintegrate as soon as

focal attention is withdrawn from an object, so that the scene-based

representation is rather ‘‘poor’’ (e.g., Rensink, 2000a; Rensink, O’Regan,

& Clark, 1997). An alternative position is that visual representations do not

necessarily disintegrate after the withdrawal of attention; rather, representa-

tions from already attended regions can be accumulated within scene-basedmemory (e.g., Hollingworth, & Henderson, 2002; Hollingworth, Williams, &

Henderson, 2001).

In summary, there is evidence that a set of implicit (i.e., preattentive), as

well as explicit, memory mechanisms are involved in the guidance of visual

search. Open questions are: How many mechanisms can be distinguished?

What is their decay time? How large is their capacity? and so on. These

questions are considered, from different perspectives, in this second section

of papers in this Special Issue.The first set of four papers (Klein & Dukewich; Horowitz; McCarley,

Kramer, Boot, Peterson, Wang, & Irwin; and Gilchrist & Harvey) are

concerned with the issue of memory-based control of covert and overt (i.e.,

oculomotor) attentional scanning in visual search.

R. Klein and K. Dukewich (2006 this issue) ask: ‘‘Does the inspector have

a memory?’’ They start with elaborating the distinction between serial and

parallel search and argue that serial search would be more efficient, in

principle, if there were a mechanism, such as IOR, for reducing reinspectionsof already scanned items. They then provide a critical review and meta-

analysis of studies that have explored whether visual search is ‘‘amnesic’’.

They conclude that it rarely is; on the other hand, there is ample evidence for

the operation of IOR in visual search. Finally, they suggest three approaches

for future research (experimental, neuropsychological, and correlational)

designed to provide convergent evidence of the role of IOR for increasing

search efficiency.

The following paper, by T. S. Horowitz (2006 this issue), asks: ‘‘Howmuch memory does visual search have?’’ The goal of this paper is less to find

a definitive answer to this question than to redefine and clarify the terms of

the debate. In particular, Horowitz proposes a formal framework, based on

the ‘‘variable memory model’’ (Arani, Karwan, & Drury, 1984), which has

three parameters*(1) encoding, (2) recall, and (3) target identificationprobability*and permits cumulative RT distribution functions to begenerated. On this basis, the model can provide a common metric for


comparing answers to the above question across different experimental

paradigms, in terms that are easy to relate to the ‘‘memory’’ literature.

The next two papers are concerned with the control oculomotor scanning

in visual search. Based on RT evidence in a novel, multiple-target visualsearch task, Horowitz and Wolfe (2001) suggested that the control of

attention during visual search is not guided by memory for which of the

items or locations in a display have already been inspected. In their

contribution, J. S. McCarley, A. F. Kramer, W. R. Boot, M. S. Peterson,

R. F. Wang, and D. E. Irwin (2006 this issue) present analyses of eye

movement data from a similar experiment, which suggest that RT effects in

the multiple-target search task are primarily due to changes in eye move-

ments, and that effects which appeared to reveal memory-free search wereproduced by changes in oculomotor scanning behaviour.

Another form of oculomotor memory revealed by the systematicity of

scan paths in visual search is examined by I. D. Gilchrist and M. Harvey

(2006 this issue). They report that, with regular grid-like displays, observers

generated more horizontal than vertical saccades. Disruption of the grid

structure modulated, but did not eliminate, this systematic scanning

component. Gilchrist and Harvey take their findings to be consistent with

the scan paths being partly determined by a ‘‘cognitive’’ strategy in visualsearch.

The next set of two papers (Olivers, Humphreys, & Braithwaite; Donk)

are concerned with the benefit deriving from a preview of one set of search

items (prior to presentation of a second set containing the target). C. N. L.

Olivers, G. W. Humphreys, and J. J. Braithwaite (2006 this issue) review a

series of experiments that provide evidence for the idea that, when new visual

objects are prioritized in the preview paradigm, old objects are inhibited by a

top-down controlled suppression mechanism (visual marking): They showthat new object prioritization depends on task settings and available

attentional resources (top-down control aspect) and that selection of new

items is impaired when these items share features with the old items (negative

carryover effects within as well as between trials; inhibitory aspect). They

then reconsider the various accounts of the preview benefit (visual marking

and alternative accounts) and conclude that these are not mutually exclusive

and that the data are best explained by a combination of mechanisms.

This theme is taken up by M. Donk (2006 this issue), who argues that theresults of recent studies cannot easily be explained by the original (Watson &

Humphreys, 1997) visual-marking account. She goes on to consider three

alternatives: Feature-based inhibition (the preview benefit is mediated by

inhibition applied at the level of feature maps), temporal segregation (the

benefit results from selective attention to one set of elements that can be

perceptually segregated, on the basis of temporal-asynchrony signals, from

another set), and onset capture (the benefit is mediated by onset signals


associated with the appearance of the new elements). She maintains that

prioritization of new over old elements is primarily caused by onset capture;

however, in line with Olivers et al. (2006 this issue), she admits that other

mechanisms may play an additional role to optimize selection of the relevantsubset of elements.

The final set of three papers (by Wolfe, Reinecke, & Brawn; Hollingworth;

Woodman & Chun) are concerned with visual memory for (natural) scenes,

short-term and long-term memory effects on search.

J. M. Wolfe, A. Reinecke, and P. Brawn (2006 this issue) investigated the

role of bottlenecks in selective attention and access to visual short-term

memory in observers’ failure to identify clearly visible changes in otherwise

stable visual displays. They found that observers failed to register a colour ororientation change in an object even if they were cued to the location of the

object prior to the change occurring. This held true even with natural

images. Furthermore, observers were unable to report changes that

happened after attention had been directed to an object and before attention

returned to that object. Wolfe et al. take these demonstrated failures to

notice or identify changes to reflect ‘‘bottlenecks’’ in two pathways from

visual input to visual experience: A ‘‘selective’’ pathway, which is responsible

for object recognition and other operations that are limited to one item or asmall group of items at any one time; and a ‘‘nonselective’’ pathway, which

supports visual experience throughout the visual field but is capable of only

a limited analysis of the input (visual short-term memory).

A. Hollingworth (2006 this issue) provides a review of recent work on the

role of visual memory in scene perception and visual search. While some

accounts (e.g., Rensink, 2000b; Wolfe, 1999) assume that coherent object

representations in visual memory are fleeting, disintegrating upon the

withdrawal of attention from an object, Hollingworth considers evidencethat visual memory supports the accumulation of information from scores of

individual objects in scenes, utilizing both visual short-term and long-term

memory. Furthermore, he reviews evidence that memory for the spatial

layout of a scene and for specific object positions can efficiently guide search

within natural scenes.

The role of working (short-term) memory and long-term memory in

visual search is further considered by G. F. Woodman and M. M. Chun

(2006 this issue). Based on a review of recent studies, they argue that, whilethe working memory system is widely assumed to play a central role in the

deployment of attention in visual search, this role is more complex than

assumed by many current models. In particular, while (object) working

memory representations of targets might be essential in guiding attention

only when the identity of the target changes frequently across trials, spatial

working memory is always required in (serial) visual search. Furthermore,

both explicit and implicit long-term memory representations have clear


influences on visual search performance, with memory traces of attended

targets and target contexts facilitating the viewing of similar scenes in future

encounters. These long-term learning effects (of statistical regularities)

deserve more prominent treatment in theoretical models.

III. Brain mechanisms of visual search

Over the past 25 years, behavioural research has produced a considerable

amount of knowledge about the functional mechanisms of visual search.

However, detailed insights into the brain mechanisms underlying search

became available only during the past 5�10 years*based on approachesthat combined behavioural experimental paradigms with methods for

measuring neuronal functions at a variety of levels: From single cell

recording through the activation of component systems to the analysis of

whole system networks. These approaches made it possible for the first time

to investigate the interplay of different brain areas in the dynamic control of

visual search.

The cognitive neuropsychology of visual search examines patients with

selective brain lesions who show specific performance deficits in visual

search, ranging from difficulties with simple feature discrimination to

impaired (working) memory for objects at already scanned locations. If

these deficits can be related to specific brain lesions, important indications

may be gained as to the role of the affected areas in visual search (e.g.,

Humphreys & Riddoch, 2001; Robertson & Eglin, 1993).

Electrophysiological approaches examine the EEG/MEG as well as event-

related potentials (ERPs) in visual search tasks, above all to reveal the time

course of the processes involved in visual search (e.g., Luck, Fan, & Hillyard,

1993; Luck & Hillyard, 1995). Specific ERP components become manifest at

different points in time, and these components may be associated, in terms

of time, with processes of preattentive and attentional processing. Depend-

ing on how accurately the neural generators of these components can be

localized, these approaches can also provide indications as to the neuronal

sites where the corresponding processes are occurring (in addition to the

time at which they occur).

Neurophysiological single-cell recording studies can provide precise

information as to both the neuronal loci and the time course of processing

in visual search (e.g., Chelazzi, Duncan, Miller, & Desimone, 1993; Motter,

1994a, 1994b; Treue & Maunsell, 1996), but they cannot reveal the interplay

among different areas involved in (controlling) the search.

However, this information can be gained by functional-imaging ap-

proaches, such as PET and fMRI. Since these methods may be employed

both for the imaging of patterns of activity across the whole brain and the


detailed measurement of activation in specific cortical areas, they can

provide information about the interplay among brain areas during the

performance of visual search tasks as well as the areas that are specifically

modulated by attention (e.g., Pollmann, Weidner, Müller, & von Cramon,2000; Rees, Frith, & Lavie, 1997; Weidner, Pollmann, Müller, & von

Cramon, 2002).

Recently, a further method: Transcranial magnetic stimulation (TMS) has

been used to more precisely examine the role of certain brain areas in visual

search (e.g., Walsh, Ellison, Asbridge, & Cowey, 1999). Whereas imaging

research is ‘‘correlative’’ in nature, TMS can be applied to ‘‘causally’’

intervene in the processing*in that the targeted application of a time-locked magnetic impulse can simulate a temporary brain ‘‘lesion’’. If theaffected areas play no causal role, then such temporary lesions should have

little direct influence on specific components of visual search.

The seven papers collected in this section of this Special Issue provide

examples of how these new approaches are used to reveal the brain

mechanisms of visual search and attentional selection.

In a programmatic paper charting the field, G. W. Humphreys, J. Hodsoll,

C. N. L. Olivers, and E. Young Yoon (2006 this issue) argue that an

integrative, cognitive-neuroscience approach can contribute not only in-formation about the neural localization of processes underlying visual

search, but also information about the functional nature of these processes.

They go on to illustrate the value of combining evidence from behavioural

studies of normal observers and studies using neuroscientific methods with

regard to two issues: First, whether search for form�colour conjunctions isconstrained by processes involved in binding across the two dimensions*work with patients with parietal lesions suggests that the answer is positive;

and second, whether the ‘‘preview benefits’’ (see Donk, 2006 this issue;Olivers, Humphreys, & Braithwaite, 2006 this issue) are simply due to onset

capture*convergent evidence from electrophysiological, brain-imaging, andneuropsychological work indicates that the answer is negative.

Also using a neuropsychological approach, L. C. Robertson and J. L.

Brooks (2006 this issue) investigated whether feature processing, as well as

feature binding (see also Humphreys, Hodsall, & Olivers, 2006 this issue), is

affected in patients with spatial-attentional impairments. They show that the

mechanisms underlying ‘‘pop out’’ continue to function in the impairedvisual field, albeit at a slowed rate.

The following two studies (Lavie & de Fockert; Pollmann, Weidner,

Müller, & von Cramon), used event-related fMRI to examine top-down

modulation of attentional capture and dimension-specific saliency coding

processes, respectively. N. Lavie and J. de Fockert (2006 this issue) report

that the presence (vs. absence) of an irrelevant colour singleton distractor in

a visual search task was not only associated with activity in superior parietal


cortex, in line with attentional capture, but was also associated with frontal

cortex activity. Moreover, behavioural interference by the singleton was

negatively correlated with frontal activity, suggesting that frontal cortex is

involved in control of singleton interference (see also Folk & Remington,2006 this issue).

S. Pollmann, R. Weidner, H. J. Müller, and D. Y. von Cramon (2006 this

issue) review the evidence of a frontoposterior network of brain areas

associated with changes, across trials, in the target-defining dimension in

singleton feature, and conjunction, search (see also Müller & Krummena-

cher, 2006 this issue). They argue that anterior prefrontal components,

reflecting transient bottom-up activation, are likely to be involved in the

detection of change and the initiation and control of cross-dimensionalattention (‘‘weight’’) shifts. However, they provide new evidence that the

attentional weighting of the target-defining dimension itself is realized in

terms of a modulation of the visual input areas processing the relevant

dimension (e.g., area V4 for colour and V5/MT for motion).

How attention modulates neural processing in one feature dimension,

namely motion, is considered by S. Treue and J. C. Martinez-Trujillo (2006

this issue). Concentrating on single-cell recordings from area MT in the

extrastriate cortex of macaque monkeys trained to perform visual tasks, theyreview evidence that, in MT, ‘‘bottom-up’’ filtering processes are tightly

integrated with ‘‘top-down’’ attentional mechanisms that together create an

integrated saliency map. This topographic representation emphasizes the

behavioural relevance of the sensory input, permitting neuronal processing

resources to be concentrated on a small subset of the incoming information.

Authors such as Treue and Martinez Trujillo and Pollmann et al. ascribe

saliency coding to areas in extrastriate cortex. This position is challenged by

Li Zhaoping and R. Snowden, (2006 this issue) who propose that V1 createsa bottom-up saliency map, where saliency of any location increases with the

firing rate of the most active V1 output cell responding to it, regardless of

the feature selectivity of the cell. Thus, for example, a red vertical bar may

have its saliency signalled by a cell tuned to red colour, or one tuned to

vertical orientation, whichever cell is the most active. This predicts

interference between colour and orientation features in texture segmentation

tasks where bottom-up processes are important. Consistent with this

prediction, Zhaoping and Snowden report that segmentation of textures oforiented bars became more difficult as the colours of the bars were randomly

drawn from larger sets of colour features.

Until recently, right posterior parietal cortex has been ascribed a pre-

eminent role in visual search. This view is disputed by J. O’Shea, N. G.

Muggleton, A. Cowey, and V. Walsh (2006 this issue), who provide a

reassessment of the roles of parietal cortex and the human frontal eye fields

(FEFs). They review recent physiological and brain-imaging evidence, and


the results of a programme of TMS studies designed to directly compare the

contributions of the parietal cortex and the FEFs in search. This leads them

to argue that the FEFs are important for some aspects of search previously

solely attributed to the parietal cortex. In particular, besides conjunction

search tasks, the right FEF is activated in singleton feature search tasks that

do not require eye movements; application of TMS to the right FEF slows

RTs on target-present trials (in contrast to parietal cortex where both target-

present and target-absent trials are affected); and search-related activation in

the FEF starts as early as 40�80 ms post stimulus onset.

IV. Neurocomputational modelling of visual search

To provide explanations of the large data base that has been accumulated

over 25 years of research on visual search, a variety of models have been

developed. One class of model is rooted in Anne Treisman’s ‘‘Feature

Integration Theory’’ (FIT; Treisman & Gelade, 1980), which assumed a two-

stage processing architecture: At the first, preattentive stage, a (limited) set

of basic features are registered in parallel across the visual field; in the

second, attentional stage, items are processed serially, one after the other.

Accordingly, feature search operates in parallel (e.g., the time required to

find a red target amongst green distractors would be independent of the

display size because the target is defined by a unique basic feature*‘‘red’’);in contrast, conjunction search operates serially (e.g., the search for a ‘‘T’’

amongst ‘‘L’’s in different orientations would increase with each additional

‘‘L’’, because the ‘‘L’’s would have to be scanned one after the other before

either the ‘‘T’’ is found or the search is terminated).

However, the strict dichotomy between parallel and serial searches

postulated by FIT was cast into doubt by findings that performance in

various search tasks could not be neatly assigned to one or the other

category. In particular, search can be relatively efficient in tasks in which

certain types of feature information can be exploited, even if the target is not

defined by a single unique feature. For example, in search for a red ‘‘O’’

amongst black ‘‘O’’s and red ‘‘N’’s, observers are able to restrict search to a

subset of display items; that is, search time is only dependent on the number

of ‘‘O’’s*the red ‘‘N’’s can be effectively excluded from the search (Egeth,Virzi, & Garbart, 1984). In addition, it is also possible to exploit multiple

features*so that, for example, search for a red vertical targets amongst redhorizontal and green vertical distractors can be performed very efficiently,

even though the target is not defined by a single feature (Wolfe, 1992).

Findings along these lines led to the development of the ‘‘Guided Search’’

model (GS; Wolfe, 1994; Wolfe et al., 1989). GS maintains the two-stage

architecture, but assumes that the serial allocation of attention can be guided


by information from preattentive stages of processing. Cave’s (1999)

‘‘FeatureGate’’ model implements similar ideas in a neural network.

Other models of search have avoided the notion of serial selection of

specific items within a two-stage architecture*by, instead, postulating asingle, parallel processing system. Information is accumulated from all items

simultaneously (e.g., Eriksen & Spencer, 1969; Kinchla, 1974), and stimulus

properties determine how fast an item can be classified or identified. Parallel

models must be able to account for the increase in RT, or, respectively, the

decrease in response accuracy, with increasing display size (without recourse

to a serial processing stage along the lines of FIT and GS). Many parallel

models assume that the ‘‘parallel processor’’ has a limited capacity (e.g.,

Bundesen, 1990); other models assume an essentially unlimited capacity.Although, according to these models, all items can be processed simulta-

neously, the result remains a signal that is embedded in a background of

noise. With increasing display size, the number of sources of noise (i.e.,

uncertainty) increases and performance declines (Palmer, Verghese, & Pavel,

2000).

Indeed, visual search may be modelled as a signal detection problem. If

the target signal is strong enough, the noise produced by the distractors will

have little influence on performance. However, if the target signal is weak,more information will have to be accumulated in order to discriminate it

from the background noise (Eckstein, Thomas, Palmer, & Shimozaki, 2000).

Signal detection models have proved to be of particular relevance for (real-

life) search tasks in which the targets are not clearly demarcated in the image

(e.g., search for tumours in radiological images; Swensson & Judy, 1981).

Recently, there has been a blurring of the distinction between the model

classes. For example, Bundesen (1998a, 1998b) proposed the parallel

processing of item groups (instead of parallel processing of all items)*aproposal that combines aspects of serial and of parallel search models (see

also Carrasco, Ponte, Rechea, & Sampedro, 1998; Grossberg, Mingolla, &

Ross, 1994; Humphreys & Müller, 1993; Treisman, 1982). Moore and Wolfe

(2001) proposed that, while items are selected serially, one at a time (like in

FIT or GS), several items can be processed simultaneously. A ‘‘pipeline’’ or

‘‘car wash’’ facility serve as relevant metaphors: Only one car can enter the

facility at a time, but several cars may be simultaneously inside it. Such a

processing system displays aspects of both serial and (capacity-limited)parallel processing (Harris, Shaw, & Bates, 1979).

Besides these formal computational models of visual search, neurocom-

putational models have been developed increasingly over the past 15 years,

to simulate processes of visual search (described by functional theories)

within neuronal network systems (e.g., Cave, 1999; Deco & Zihl, 2000;

Heinke, Humphreys, & di Virgilio, 2002; Humphreys & Müller, 1993; Itti &

Koch, 2001). In as far as these models incorporate neuroscientifically


founded assumptions with regard to the functional architecture of the search

processes (and thus display a certain degree of ‘‘realism’’), they bridge the

gap between behavioural experimental and computational research on the

one hand and neuroscientific research on the other.This final section ‘‘Neurocomputational modelling of visual search’’

brings together three papers (Itti; Heinke, Humphreys, & Tweed; Deco &

Zihl) that represent three major approaches to the modelling of visual search

and illustrate the power of these approaches in accounting for visual-

selection phenomena.

L. Itti (2006 this issue) provides an extension of his saliency-based

(simulation) model of attentional allocation by investigating whether an

increased realism in the simulations would improve the prediction of wherehuman observers direct their gaze while watching video clips. Simulation

realism was achieved by augmenting a basic version of the model with a

gaze-contingent foveation filter, or by embedding the video frames within a

larger background and shifting them to eye position. Model-predicted

salience was determined for locations gazed at by the observers, compared to

random locations. The results suggest that emulating the details of visual

stimulus processing improves the fit between the model prediction and the

gaze behaviour of human observers.D. Heinke, G. W. Humphreys, and C. L. Tweed (2006 this issue) present

an extended version of their ‘‘Selective Attention for Identification Model’’

(SAIM) incorporating a feature extraction mechanism. Heinke et al. show

that the revised SAIM can simulate both efficient and inefficient human

search as well as search asymmetries, while maintaining translation-invariant

object identification. Heinke et al. then turn to the simulation of top-down

modulatory effects on search performance reported in recent studies, and

they present an experimental test of a novel model prediction. Thesimulations demonstrate the importance of top-down target expectancies

for selection time and accuracy. Also, consistent with the model prediction, a

priming experiment with human observers revealed overall RT and search

rate effects for valid-prime relative to neutral- and invalid-prime conditions.

A powerful, ‘‘neurodynamic model’’ of the function of attention and

memory in visual processing has been developed by G. Deco and his

colleagues, based on Desimone and Duncan’s (1995) ‘‘biased competition

hypothesis’’. G. Deco and J. Zihl (2006 this issue) describe the scope of thismodel, which integrates, within a unifying framework, the explanation of

several existing types of experimental data obtained at different levels of

investigation. At the microscopic level, single-cell recordings are simulated;

at the mesoscopic level of cortical areas, results of fMRI studies are

reproduced; and at the macroscopic level, the behavioural performance in

psychophysical experiments, such as visual-search tasks, is described by the

model. In particular, the model addresses how bottom-up and top-down


(attentional) processes interact in visual processing, with attentional top-

down bias guiding the dynamics to focus attention at a given location or on a

set of features. Importantly, the modelling suggests that some seemingly

serial processes reflect the operation of interacting parallel distributedsystems.

REFERENCES

Arani, T., Karwan, M. H., & Drury, C. G. (1984). A variable-memory model of visual search.

Human Factors, 26 , 631�639.Bacon, W. F., & Egeth, H. E. (1997). Goal-directed guidance of attention: Evidence from

conjunctive visual search. Journal of Experimental Psychology: Human Perception and

Performance, 23 , 948�961.Baylis, G. C., & Driver, J. (1992). Visual parsing and response competition: The effect of

grouping factors. Perception and Psychophysics , 51 , 45�162.Bundesen, C. (1990). A theory of visual attention. Psychological Review, 97 , 523�547.Bundesen, C. (1998a). A computational theory of visual attention. Philosophical Transactions of

the Royal Society London, Series B: Biological Sciences, 353 , 1271�1281.Bundesen, C. (1998b). Visual selective attention: Outlines of a choice model, a race model, and a

computational theory. Visual Cognition , 5 , 287�309.Carrasco, M., Ponte, D., Rechea, C., & Sampedro, M. J. (1998). ‘‘Transient structures’’: The

effects of practice and distractor grouping on a within-dimension conjunction search.

Perception and Psychophysics, 60 , 1243�1258.Cave, K. (1999). The FeatureGate model of visual selection. Psychological Research , 62 , 182�

194.

Chelazzi, L., Duncan, J., Miller, E., & Desimone, R. (1993). A neural basis for visual search in

inferior temporal cortex. Nature , 363 , 345�347.Chen, L., & Zhou, W. (1997). Holes in illusory conjunctions. Perception and Psychophysics, 4 ,

507�511.Chun, M. M., & Jiang, Y. (1998). Contextual cueing: Implicit learning and memory of visual

context guides spatial attention. Cognitive Psychology, 36 , 28�71.Cohen, A., & Magen, H. (1999). Intra- and cross-dimensional visual search for single feature

targets. Perception and Psychophysics, 61 , 291�307.Deco, G., & Zihl, J. (2000). Neurodynamical mechanism of binding and selective attention for

visual search. Neurocomputing , 32�33 , 693�699.Desimone, R., & Duncan, J. (1995). Neural mechanisms of selective visual attention. Annual

Review of Neuroscience, 18 , 193�222.Donk, M., & Theeuwes, J. (2001). Visual marking beside the mark: Prioritizing selection by

abrupt onsets. Perception and Psychophysics, 63 , 891�900.Eckstein, M. P., Thomas, J. P., Palmer, J., & Shimozaki, S. S. (2000). A signal detection model

predicts the effects of set size on visual search accuracy for feature, conjunction, triple

conjunction, and disjunction displays. Perception and Psychophysics , 62 , 425�451.Egeth, H. E., Virzi, R. A., & Garbart, H. (1984). Searching for conjunctively defined targets.

Journal of Experimental Psychology: Human Perception and Performance, 10 , 32�39.Egly, R., Driver, J., & Rafal, R. D. (1994). Shifting visual attention between objects and

locations: Evidence from normal and parietal lesion subjects. Journal of Experimental

Psychology: General , 123 , 161�177.Enns, J. T., & Rensink, R. A. (1990). Influence of scene-based properties in visual search.

Science, 247 , 721�723.


Eriksen, C. W., & Spencer, T. (1969). Rate of information processing in visual perception: Some

results and methodological considerations. Journal of Experimental Psychology, 79 , 1�16.Folk, C. L., & Remington, R. (1998). Selectivity in distraction by irrelevant featural singletons:

Evidence for two forms of attentional capture. Journal of Experimental Psychology: Human

Perception and Performance, 24 , 847�858.Fox, E., Lester, V., Russo, R., Bowles, R. J., Pichler, A., & Dutton, K. (2000). Facial expressions

of emotion: Are angry faces detected more efficiently? Cognition and Emotion , 14 , 61�92.Gilchrist, I. D., & Harvey, M. (2000). Refixation frequency and memory mechanisms in visual

search. Current Biology, 10 , 1209�1212.Grossberg, S., Mingolla, E., & Ross, W. D. (1994). A neural theory of attentive visual search:

Interactions of boundary, surface, spatial and object representations. Psychological Review,

101 , 470�489.Harms, L., & Bundesen, C. (1983). Color segregation and selective attention in a nonsearch

task. Perception and Psychophysics, 33 , 11�19.Harris, J. R., Shaw, M. L., & Bates, M. (1979). Visual search in multicharacter arrays with and

without gaps. Perception and Psychophysics, 26 , 69�84.He, Z. J., & Nakayama, K. (1992). Surface versus features in visual search. Nature , 359 , 231�

233.

Heinke, D., Humphreys, G. W., & di Virgilio, G. (2002). Modelling visual search experiments:

The selective attention for identification model (SAIM). Neurocomputing , 44�46 , 817�822.Hollingworth, A., & Henderson, J. M. (2002). Accurate visual memory for previously attended

objects in natural scenes. Journal of Experimental Psychology: Human Perception and

Performance, 28 , 113�136.Hollingworth, A., Williams, C. C., & Henderson, J. M. (2001). To see and remember: Visually

specific information is retained in memory from previously attended objects in natural

scenes. Psychonomic Bulletin and Review, 8 , 761�768.Horowitz, T. S., & Wolfe, J. M. (1998). Visual search has no memory. Nature , 394 , 575�577.Horowitz, T. S., & Wolfe, J. M. (2001). Search for multiple targets: Remember the targets, forget

the search. Perception and Psychophysics, 63 , 272�285.Humphreys, G. W., & Müller, H. J. (1993). SEarch via Recursive Rejection (SERR): A

connectionist model of visual search. Cognitive Psychology, 25 , 43�110.Humphreys, G. W., & Riddoch, M. J. (2001). Detection by action: Evidence for affordances in

search in neglect. Nature Neuroscience, 4 , 84�88.Itti, L., & Koch, C. (2001). Computational modelling of visual attention. Nature Neuroscience

Reviews , 2 , 194�203.Jiang, Y., Chun, M. M., & Marks, L. E. (2002). Visual marking: Selective attention to

asynchronous temporal groups. Journal of Experimental Psychology: Human Perception and

Performance, 28 , 717�730.Kim, M.-S., & Cave, K. R. (1999). Grouping effects on spatial attention in visual search. Journal

of General Psychology, 126 , 326�352.Kinchla, R. A. (1974). Detecting targets in multi-element arrays: A confusability model.

Perception and Psychophysics, 15 , 149�158.Klein, R. (1988). Inhibitory tagging system facilitates visual search. Nature , 334 , 430�431.Kramer, A. F., & Watson, S. E. (1996). Object-based visual selection and the principle of

uniform connectedness. In A. F. Kramer, M. G. H. Coles, & G. Logan (Eds.), Converging

operations in the study of visual selective attention (pp. 395�414). Washington, DC:American Psychological Association.

Krummenacher, J., Müller, H. J., & Heller, D. (2001). Visual search for dimensionally redundant

pop-out targets: Evidence for parallel-coactive processing of dimensions. Perception and

Psychophysics, 63 , 901�917.


Kumada, T., & Humphreys, G. W. (2002). Cross-dimensional interference and cross-trial

inhibition. Perception and Psychophysics, 64 , 493�503.Luck, S. J., Fan, S., & Hillyard, S. A. (1993). Attention-related modulation of sensory-evoked

brain activity in a visual search task. Journal of Cognitive Neuroscience, 5 , 188�195.Luck, S. J., & Hillyard, S. A. (1995). The role of attention in feature detection and conjunction

discrimination: An electrophysiological analysis. International Journal of Neuroscience , 80 ,

281�297.Maljkovic, V., & Nakayama, K. (1994). Priming of pop-out: I. Role of features. Memory and

Cognition , 22 , 657�672.Maljkovic, V., & Nakayama, K. (1996). Priming of pop-out: II. The role of position. Perception

and Psychophysics, 58 , 977�991.Maljkovic, V., & Nakayama, K. (2000). Priming of popout: III. A short-term implicit memory

system beneficial for rapid target selection. Visual Cognition , 7 , 571�595.Moore, C. M., & Wolfe, J. M. (2001). Getting beyond the serial/parallel debate in visual search:

A hybrid approach. In K. Shapiro (Ed.), The limits of attention: Temporal constraints on

human information processing (pp. 178�198). Oxford, UK: Oxford University Press.Motter, B. C. (1994a). Neural correlates of attentive selection for color and luminance in

extrastriate area V4. Journal of Neuroscience, 14 , 2178�2189.Motter, B. C. (1994b). Neural correlates of feature selective memory and pop-out in extrastriate

area V4. Journal of Neuroscience, 14 , 2190�2199.Müller, H. J., Heller, D., & Ziegler, J. (1995). Visual search for singleton feature targets within

and across feature dimensions. Perception and Psychophysics, 57 , 1�17.Müller, H. J., Reimann, B., & Krummenacher, J. (2003). Visual search for singleton feature

targets across dimensions: Stimulus- and expectancy-driven effects in dimensional weighting.

Journal of Experimental Psychology: Human Perception and Performance, 29 , 1021�1035.Müller, H. J., & von Mühlenen, A. (2000). Probing distractor inhibition in visual search:

Inhibition of return. Journal of Experimental Psychology: Human Perception and Perfor-

mance, 26 , 1591�1605.Neisser, U. (1967). Cognitive psychology. New York: Appleton-Century-Crofts.

Nothdurft, H. C. (1991). Texture segmentation and pop-out from orientation contrast. Vision

Research , 31 , 1073�1078..Öhman, A., Lundqvist, D., & Esteves, F. (2001). The face in the crowd revisited: A threat

advantage with schematic stimuli. Journal of Personality and Social Psychology, 80 , 381�396.

Palmer, J., Verghese, P., & Pavel, M. (2000). The psychophysics of visual search. Vision Research ,

40 , 1227�1268.Peterson, M. S., Kramer, A. F., Wang, R. F., Irwin, D. E., & McCarley, J. S. (2001). Visual

search has memory. Psychological Science, 12 , 287�292.Pollmann, S., Weidner, R., Müller, H. J., & von Cramon, D.Y. (2000). A fronto-posterior

network involved in visual dimension changes. Journal of Cognitive Neuroscience , 12 , 480�494.

Posner, M. I., & Cohen, Y. (1984). Components of visual orienting. In H. Bouma & D. G.

Bouwhuis (Eds.), Attention and performance X: Control of language processes (pp. 531�556).Hove, UK: Lawrence Erlbaum Associates Ltd.

Rees, G., Frith, C. D., & Lavie, N. (1997). Modulating irrelevant motion perception by varying

attentional load in an unrelated task. Science, 278 , 1616�1619.Rensink, R. A. (2000a). The dynamic representation of scenes. Visual Cognition , 7 , 17�42.Rensink, R. A. (2000b). Seeing, sensing, and scrutinizing. Vision Research , 40 , 1469�1487.Rensink, E. A., O’Regan, J. K., & Clark, J. J. (1997). To see or not to see: The need for attention

to perceive changes in scenes. Psychological Science, 8 , 368�373.


Robertson, L. C., & Eglin, M. (1993). Attentional search in unilateral visual neglect. In I. H.

Robertson & J. C. Marshall (Eds.), Unilateral neglect: Clinical and experimental findings (pp.

169�191). Hove, UK: Lawrence Erlbaum Associates Ltd.Swensson, R. G., & Judy, P. F. (1981). Detection of noisy visual targets: Models for the effects of

spatial uncertainty and signal-to-noise ratio. Perception and Psychophysics, 29 , 521�534.Takeda, Y., & Yagi, A. (2000). Inhibitory tagging in visual search can be found if search stimuli

remain visible. Perception and Psychophysics, 62 , 927�934.Theeuwes, J. (1991). Cross-dimensional perceptual selectivity. Perception and Psychophysics , 50 ,

184�193.Theeuwes, J. (1992). Perceptual selectivity for color and form. Perception and Psychophysics, 51 ,

599�606.Treisman, A. (1982). Perceptual grouping and attention in visual search for features and for

objects. Journal of Experimental Psychology: Human Perception and Performance, 8 , 194�214.

Treisman, A., & Gelade, G. (1980). A feature-integration theory of attention. Cognitive

Psychology, 12 , 97�136.Treue, S., & Maunsell, J. H. R. (1996). Attentional modulation of visual motion processing in

cortical areas MT and MST. Nature , 382 , 539�541.Walsh, V., Ellison, A., Asbridge, E., & Cowey, A. (1999). The role of the parietal cortex in visual

attention: Hemispheric asymmetries and the effects of learning: A magnetic stimulation

study. Neuropsychologia , 37 , 245�251.Watson, D. G., & Humphreys, G. W. (1997). Visual marking: Prioritizing selection for new

objects by top-down attentional inhibition of old objects. Psychological Review, 104 , 90�122.

Weidner, R., Pollmann, S., Müller, H. J., & von Cramon, D. Y. (2002). Top-down controlled

visual dimension weighting: An event-related fMRI study. Cerebral Cortex , 12 , 318�328.Wolfe, J. M. (1992). ‘‘Effortless’’ texture segmentation and ‘‘parallel’’ visual search are not the

same thing. Vision Research , 32 , 757�763.Wolfe, J. M. (1994). Guided Search 2.0: A revised model of visual search. Psychonomic Bulletin

and Review, 1 , 202�238.Wolfe, J. M. (1998). What can 1,000,000 trials tell us about visual search? Psychological Science,

9 , 33�39.Wolfe, J. M. (1999). Inattentional amnesia. In V. Coltheart (Ed.), Fleeting memories (pp. 71�94).

Cambridge, MA: MIT Press.

Wolfe, J. M., Cave, K. R., & Franzel, S. L. (1989). Guided Search: An alternative to the feature

integration model for visual search. Journal of Experimental Psychology: Human Perception

and Performance, 15 , 419�433.Yantis, S. (1993). Stimulus-driven attentional capture. Current Directions in Psychological

Science, 2 , 156�161.


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